Overview

Dataset statistics

Number of variables19
Number of observations475740
Missing cells1427720
Missing cells (%)15.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory189.6 MiB
Average record size in memory417.9 B

Variable types

Numeric13
Categorical6

Warnings

Series has constant value "EQ" Constant
Date has a high cardinality: 5306 distinct values High cardinality
Symbol has a high cardinality: 66 distinct values High cardinality
Prev Close is highly correlated with Open and 5 other fieldsHigh correlation
Open is highly correlated with Prev Close and 5 other fieldsHigh correlation
High is highly correlated with Prev Close and 5 other fieldsHigh correlation
Low is highly correlated with Prev Close and 5 other fieldsHigh correlation
Last is highly correlated with Prev Close and 5 other fieldsHigh correlation
Close is highly correlated with Prev Close and 5 other fieldsHigh correlation
VWAP is highly correlated with Prev Close and 5 other fieldsHigh correlation
Turnover is highly correlated with Trades and 1 other fieldsHigh correlation
Trades is highly correlated with Turnover and 1 other fieldsHigh correlation
Deliverable Volume is highly correlated with Turnover and 1 other fieldsHigh correlation
Prev Close is highly correlated with Open and 5 other fieldsHigh correlation
Open is highly correlated with Prev Close and 5 other fieldsHigh correlation
High is highly correlated with Prev Close and 5 other fieldsHigh correlation
Low is highly correlated with Prev Close and 5 other fieldsHigh correlation
Last is highly correlated with Prev Close and 5 other fieldsHigh correlation
Close is highly correlated with Prev Close and 5 other fieldsHigh correlation
VWAP is highly correlated with Prev Close and 5 other fieldsHigh correlation
Turnover is highly correlated with Deliverable VolumeHigh correlation
Deliverable Volume is highly correlated with TurnoverHigh correlation
Prev Close is highly correlated with Open and 5 other fieldsHigh correlation
Open is highly correlated with Prev Close and 5 other fieldsHigh correlation
High is highly correlated with Prev Close and 5 other fieldsHigh correlation
Low is highly correlated with Prev Close and 5 other fieldsHigh correlation
Last is highly correlated with Prev Close and 5 other fieldsHigh correlation
Close is highly correlated with Prev Close and 5 other fieldsHigh correlation
VWAP is highly correlated with Prev Close and 5 other fieldsHigh correlation
High is highly correlated with Close and 6 other fieldsHigh correlation
Deliverable Volume is highly correlated with Turnover and 1 other fieldsHigh correlation
df_index is highly correlated with SymbolHigh correlation
Trades is highly correlated with Turnover and 1 other fieldsHigh correlation
ISIN Code is highly correlated with Industry and 2 other fieldsHigh correlation
Close is highly correlated with High and 6 other fieldsHigh correlation
VWAP is highly correlated with High and 6 other fieldsHigh correlation
%Deliverble is highly correlated with SymbolHigh correlation
Low is highly correlated with High and 6 other fieldsHigh correlation
Industry is highly correlated with ISIN Code and 2 other fieldsHigh correlation
Company Name is highly correlated with ISIN Code and 2 other fieldsHigh correlation
Symbol is highly correlated with High and 11 other fieldsHigh correlation
Open is highly correlated with High and 6 other fieldsHigh correlation
Turnover is highly correlated with Deliverable Volume and 1 other fieldsHigh correlation
Prev Close is highly correlated with High and 6 other fieldsHigh correlation
Volume is highly correlated with Deliverable Volume and 1 other fieldsHigh correlation
Last is highly correlated with High and 6 other fieldsHigh correlation
Series is highly correlated with Company Name and 3 other fieldsHigh correlation
Company Name is highly correlated with Series and 3 other fieldsHigh correlation
Industry is highly correlated with Series and 3 other fieldsHigh correlation
Symbol is highly correlated with Series and 3 other fieldsHigh correlation
ISIN Code is highly correlated with Series and 3 other fieldsHigh correlation
Company Name has 475690 (> 99.9%) missing values Missing
Industry has 475690 (> 99.9%) missing values Missing
ISIN Code has 475690 (> 99.9%) missing values Missing
Company Name is uniformly distributed Uniform
ISIN Code is uniformly distributed Uniform

Reproduction

Analysis started2021-08-30 12:32:33.363041
Analysis finished2021-08-30 12:35:22.082704
Duration2 minutes and 48.72 seconds
Software versionpandas-profiling v3.0.0
Download configurationconfig.json

Variables

df_index
Real number (ℝ≥0)

HIGH CORRELATION

Distinct235192
Distinct (%)49.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean59383.2172
Minimum0
Maximum235191
Zeros52
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size3.6 MiB
2021-08-30T18:05:22.280535image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile465
Q12331
median5053
Q3116256.25
95-th percentile211404.05
Maximum235191
Range235191
Interquartile range (IQR)113925.25

Descriptive statistics

Standard deviation74787.62299
Coefficient of variation (CV)1.259406723
Kurtosis-0.5627300011
Mean59383.2172
Median Absolute Deviation (MAD)4809
Skewness0.9612231003
Sum2.825097175 × 1010
Variance5593188553
MonotonicityNot monotonic
2021-08-30T18:05:22.491414image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
052
 
< 0.1%
2152
 
< 0.1%
2052
 
< 0.1%
4452
 
< 0.1%
1952
 
< 0.1%
4552
 
< 0.1%
1852
 
< 0.1%
4652
 
< 0.1%
1752
 
< 0.1%
4752
 
< 0.1%
Other values (235182)475220
99.9%
ValueCountFrequency (%)
052
< 0.1%
152
< 0.1%
252
< 0.1%
352
< 0.1%
452
< 0.1%
552
< 0.1%
652
< 0.1%
752
< 0.1%
852
< 0.1%
952
< 0.1%
ValueCountFrequency (%)
2351911
< 0.1%
2351901
< 0.1%
2351891
< 0.1%
2351881
< 0.1%
2351871
< 0.1%
2351861
< 0.1%
2351851
< 0.1%
2351841
< 0.1%
2351831
< 0.1%
2351821
< 0.1%

Date
Categorical

HIGH CARDINALITY

Distinct5306
Distinct (%)1.1%
Missing50
Missing (%)< 0.1%
Memory size30.4 MiB
2016-11-11
 
99
2011-01-31
 
99
2012-05-29
 
99
2015-11-17
 
99
2014-11-28
 
99
Other values (5301)
475195 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters4756900
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2000-01-03
2nd row2000-01-04
3rd row2000-01-05
4th row2000-01-06
5th row2000-01-07

Common Values

ValueCountFrequency (%)
2016-11-1199
 
< 0.1%
2011-01-3199
 
< 0.1%
2012-05-2999
 
< 0.1%
2015-11-1799
 
< 0.1%
2014-11-2899
 
< 0.1%
2012-01-3199
 
< 0.1%
2018-10-2599
 
< 0.1%
2015-06-1699
 
< 0.1%
2014-03-1099
 
< 0.1%
2014-04-0799
 
< 0.1%
Other values (5296)474700
99.8%

Length

2021-08-30T18:05:22.930162image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2016-11-1199
 
< 0.1%
2012-06-2299
 
< 0.1%
2013-02-0699
 
< 0.1%
2013-10-2599
 
< 0.1%
2014-12-2499
 
< 0.1%
2012-03-1499
 
< 0.1%
2020-10-2399
 
< 0.1%
2011-10-2899
 
< 0.1%
2012-04-2699
 
< 0.1%
2018-04-1099
 
< 0.1%
Other values (5296)474700
99.8%

Most occurring characters

ValueCountFrequency (%)
01322344
27.8%
-951380
20.0%
2831406
17.5%
1701967
14.8%
3155567
 
3.3%
7135732
 
2.9%
8135101
 
2.8%
9132529
 
2.8%
6132070
 
2.8%
5130732
 
2.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3805520
80.0%
Dash Punctuation951380
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
01322344
34.7%
2831406
21.8%
1701967
18.4%
3155567
 
4.1%
7135732
 
3.6%
8135101
 
3.6%
9132529
 
3.5%
6132070
 
3.5%
5130732
 
3.4%
4128072
 
3.4%
Dash Punctuation
ValueCountFrequency (%)
-951380
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common4756900
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
01322344
27.8%
-951380
20.0%
2831406
17.5%
1701967
14.8%
3155567
 
3.3%
7135732
 
2.9%
8135101
 
2.8%
9132529
 
2.8%
6132070
 
2.8%
5130732
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII4756900
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
01322344
27.8%
-951380
20.0%
2831406
17.5%
1701967
14.8%
3155567
 
3.3%
7135732
 
2.9%
8135101
 
2.8%
9132529
 
2.8%
6132070
 
2.8%
5130732
 
2.7%

Symbol
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION

Distinct66
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size29.0 MiB
CIPLA
 
10613
IOC
 
10613
BPCL
 
10613
ICICIBANK
 
10613
RELIANCE
 
10613
Other values (61)
422675 

Length

Max length10
Median length7
Mean length6.873708328
Min length2

Characters and Unicode

Total characters3270098
Distinct characters28
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowZEETELE
2nd rowZEETELE
3rd rowZEETELE
4th rowZEETELE
5th rowZEETELE

Common Values

ValueCountFrequency (%)
CIPLA10613
 
2.2%
IOC10613
 
2.2%
BPCL10613
 
2.2%
ICICIBANK10613
 
2.2%
RELIANCE10613
 
2.2%
GRASIM10613
 
2.2%
WIPRO10613
 
2.2%
ITC10613
 
2.2%
HDFCBANK10613
 
2.2%
TITAN10613
 
2.2%
Other values (56)369610
77.7%

Length

2021-08-30T18:05:23.403890image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
hdfcbank10613
 
2.2%
icicibank10613
 
2.2%
reliance10613
 
2.2%
itc10613
 
2.2%
grasim10613
 
2.2%
m&m10613
 
2.2%
drreddy10613
 
2.2%
titan10613
 
2.2%
ioc10613
 
2.2%
ongc10613
 
2.2%
Other values (56)369610
77.7%

Most occurring characters

ValueCountFrequency (%)
A356723
 
10.9%
I319358
 
9.8%
N258950
 
7.9%
T256672
 
7.8%
E235830
 
7.2%
C224719
 
6.9%
R180890
 
5.5%
O168123
 
5.1%
S160913
 
4.9%
L149536
 
4.6%
Other values (18)958384
29.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter3249242
99.4%
Other Punctuation10613
 
0.3%
Dash Punctuation6405
 
0.2%
Decimal Number3838
 
0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A356723
 
11.0%
I319358
 
9.8%
N258950
 
8.0%
T256672
 
7.9%
E235830
 
7.3%
C224719
 
6.9%
R180890
 
5.6%
O168123
 
5.2%
S160913
 
5.0%
L149536
 
4.6%
Other values (15)937528
28.9%
Dash Punctuation
ValueCountFrequency (%)
-6405
100.0%
Decimal Number
ValueCountFrequency (%)
03838
100.0%
Other Punctuation
ValueCountFrequency (%)
&10613
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin3249242
99.4%
Common20856
 
0.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
A356723
 
11.0%
I319358
 
9.8%
N258950
 
8.0%
T256672
 
7.9%
E235830
 
7.3%
C224719
 
6.9%
R180890
 
5.6%
O168123
 
5.2%
S160913
 
5.0%
L149536
 
4.6%
Other values (15)937528
28.9%
Common
ValueCountFrequency (%)
&10613
50.9%
-6405
30.7%
03838
 
18.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII3270098
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A356723
 
10.9%
I319358
 
9.8%
N258950
 
7.9%
T256672
 
7.8%
E235830
 
7.2%
C224719
 
6.9%
R180890
 
5.5%
O168123
 
5.1%
S160913
 
4.9%
L149536
 
4.6%
Other values (18)958384
29.3%

Series
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size26.8 MiB
EQ
475740 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters951480
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEQ
2nd rowEQ
3rd rowEQ
4th rowEQ
5th rowEQ

Common Values

ValueCountFrequency (%)
EQ475740
100.0%

Length

2021-08-30T18:05:23.778675image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-08-30T18:05:23.888619image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
eq475740
100.0%

Most occurring characters

ValueCountFrequency (%)
E475740
50.0%
Q475740
50.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter951480
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E475740
50.0%
Q475740
50.0%

Most occurring scripts

ValueCountFrequency (%)
Latin951480
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
E475740
50.0%
Q475740
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII951480
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E475740
50.0%
Q475740
50.0%

Prev Close
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct63729
Distinct (%)13.4%
Missing50
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean1255.122428
Minimum0
Maximum32861.95
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size3.6 MiB
2021-08-30T18:05:24.017554image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile97.25
Q1271.1
median558.125
Q31230.75
95-th percentile3944.8
Maximum32861.95
Range32861.95
Interquartile range (IQR)959.65

Descriptive statistics

Standard deviation2569.114743
Coefficient of variation (CV)2.046903702
Kurtosis49.37165031
Mean1255.122428
Median Absolute Deviation (MAD)363.175
Skewness6.308670799
Sum597049187.6
Variance6600350.564
MonotonicityNot monotonic
2021-08-30T18:05:24.226420image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13767
 
< 0.1%
14063
 
< 0.1%
13563
 
< 0.1%
200.1561
 
< 0.1%
3060
 
< 0.1%
125.260
 
< 0.1%
157.160
 
< 0.1%
270.0559
 
< 0.1%
28158
 
< 0.1%
11258
 
< 0.1%
Other values (63719)475081
99.9%
ValueCountFrequency (%)
02
 
< 0.1%
22
 
< 0.1%
9.152
 
< 0.1%
9.42
 
< 0.1%
9.52
 
< 0.1%
9.62
 
< 0.1%
9.6512
< 0.1%
9.756
< 0.1%
9.84
 
< 0.1%
9.854
 
< 0.1%
ValueCountFrequency (%)
32861.952
< 0.1%
32766.12
< 0.1%
32661.852
< 0.1%
32617.752
< 0.1%
32498.62
< 0.1%
32443.52
< 0.1%
32403.952
< 0.1%
32371.052
< 0.1%
32233.42
< 0.1%
32226.42
< 0.1%

Open
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct44298
Distinct (%)9.3%
Missing50
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean1256.67471
Minimum8.5
Maximum33399.95
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.6 MiB
2021-08-30T18:05:24.454289image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum8.5
5-th percentile97.4
Q1271.5
median558.7
Q31231.15
95-th percentile3950
Maximum33399.95
Range33391.45
Interquartile range (IQR)959.65

Descriptive statistics

Standard deviation2572.983975
Coefficient of variation (CV)2.04745425
Kurtosis49.42402029
Mean1256.67471
Median Absolute Deviation (MAD)363.4
Skewness6.311702782
Sum597787592.6
Variance6620246.537
MonotonicityNot monotonic
2021-08-30T18:05:24.664169image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
340423
 
0.1%
370421
 
0.1%
330412
 
0.1%
350398
 
0.1%
290398
 
0.1%
315394
 
0.1%
360389
 
0.1%
260388
 
0.1%
345381
 
0.1%
270376
 
0.1%
Other values (44288)471710
99.2%
ValueCountFrequency (%)
8.52
 
< 0.1%
9.52
 
< 0.1%
9.62
 
< 0.1%
9.652
 
< 0.1%
9.758
< 0.1%
9.84
< 0.1%
9.858
< 0.1%
9.94
< 0.1%
9.954
< 0.1%
106
< 0.1%
ValueCountFrequency (%)
33399.952
< 0.1%
328002
< 0.1%
326982
< 0.1%
325992
< 0.1%
325004
< 0.1%
324992
< 0.1%
324892
< 0.1%
324802
< 0.1%
32451.52
< 0.1%
32301.22
< 0.1%

High
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct49036
Distinct (%)10.3%
Missing50
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean1275.34912
Minimum9.75
Maximum33480
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.6 MiB
2021-08-30T18:05:24.897036image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum9.75
5-th percentile99.4
Q1276
median568.45
Q31250
95-th percentile3999
Maximum33480
Range33470.25
Interquartile range (IQR)974

Descriptive statistics

Standard deviation2607.211411
Coefficient of variation (CV)2.044311923
Kurtosis49.21476336
Mean1275.34912
Median Absolute Deviation (MAD)369.15
Skewness6.301985792
Sum606670822.8
Variance6797551.344
MonotonicityNot monotonic
2021-08-30T18:05:25.108914image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
360256
 
0.1%
375252
 
0.1%
350251
 
0.1%
330249
 
0.1%
275248
 
0.1%
370247
 
0.1%
315243
 
0.1%
355239
 
0.1%
280239
 
0.1%
305234
 
< 0.1%
Other values (49026)473232
99.5%
ValueCountFrequency (%)
9.752
 
< 0.1%
9.88
< 0.1%
9.854
< 0.1%
9.96
< 0.1%
9.952
 
< 0.1%
108
< 0.1%
10.14
< 0.1%
10.154
< 0.1%
10.22
 
< 0.1%
10.252
 
< 0.1%
ValueCountFrequency (%)
334802
< 0.1%
33328.82
< 0.1%
32916.852
< 0.1%
32799.22
< 0.1%
32766.62
< 0.1%
327502
< 0.1%
32698.952
< 0.1%
32644.952
< 0.1%
32604.252
< 0.1%
325492
< 0.1%

Low
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct51335
Distinct (%)10.8%
Missing50
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean1236.558405
Minimum8.5
Maximum32468.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.6 MiB
2021-08-30T18:05:25.345778image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum8.5
5-th percentile95.25
Q1266.25
median549
Q31210.25
95-th percentile3891.05
Maximum32468.1
Range32459.6
Interquartile range (IQR)944

Descriptive statistics

Standard deviation2534.53152
Coefficient of variation (CV)2.049665838
Kurtosis49.53362423
Mean1236.558405
Median Absolute Deviation (MAD)357.35
Skewness6.315493272
Sum588218467.9
Variance6423850.027
MonotonicityNot monotonic
2021-08-30T18:05:25.552660image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
300274
 
0.1%
325263
 
0.1%
370252
 
0.1%
320251
 
0.1%
310244
 
0.1%
390240
 
0.1%
350239
 
0.1%
305230
 
< 0.1%
260228
 
< 0.1%
330225
 
< 0.1%
Other values (51325)473244
99.5%
ValueCountFrequency (%)
8.52
 
< 0.1%
8.952
 
< 0.1%
9.12
 
< 0.1%
9.152
 
< 0.1%
9.32
 
< 0.1%
9.52
 
< 0.1%
9.556
< 0.1%
9.66
< 0.1%
9.656
< 0.1%
9.72
 
< 0.1%
ValueCountFrequency (%)
32468.12
< 0.1%
324002
< 0.1%
323012
< 0.1%
32300.052
< 0.1%
323002
< 0.1%
322902
< 0.1%
322002
< 0.1%
32136.852
< 0.1%
32082.252
< 0.1%
318502
< 0.1%

Last
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct48570
Distinct (%)10.2%
Missing50
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean1255.309781
Minimum9.1
Maximum32849
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.6 MiB
2021-08-30T18:05:26.262253image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum9.1
5-th percentile97.25
Q1271.2
median558
Q31231
95-th percentile3945
Maximum32849
Range32839.9
Interquartile range (IQR)959.8

Descriptive statistics

Standard deviation2569.138277
Coefficient of variation (CV)2.046616952
Kurtosis49.356176
Mean1255.309781
Median Absolute Deviation (MAD)363
Skewness6.307437085
Sum597138309.7
Variance6600471.489
MonotonicityNot monotonic
2021-08-30T18:05:26.471133image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
400258
 
0.1%
330255
 
0.1%
380254
 
0.1%
350253
 
0.1%
340249
 
0.1%
325241
 
0.1%
345241
 
0.1%
315238
 
0.1%
300233
 
< 0.1%
310229
 
< 0.1%
Other values (48560)473239
99.5%
ValueCountFrequency (%)
9.12
 
< 0.1%
9.252
 
< 0.1%
9.52
 
< 0.1%
9.68
< 0.1%
9.652
 
< 0.1%
9.74
< 0.1%
9.752
 
< 0.1%
9.84
< 0.1%
9.854
< 0.1%
9.94
< 0.1%
ValueCountFrequency (%)
328492
< 0.1%
327602
< 0.1%
326002
< 0.1%
325982
< 0.1%
325202
< 0.1%
324502
< 0.1%
324302
< 0.1%
324202
< 0.1%
32208.552
< 0.1%
321842
< 0.1%

Close
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct63739
Distinct (%)13.4%
Missing50
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean1255.47453
Minimum9.15
Maximum32861.95
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.6 MiB
2021-08-30T18:05:26.695020image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum9.15
5-th percentile97.3
Q1271.15
median558.3
Q31230.95
95-th percentile3945.35
Maximum32861.95
Range32852.8
Interquartile range (IQR)959.8

Descriptive statistics

Standard deviation2569.882386
Coefficient of variation (CV)2.046941077
Kurtosis49.35739553
Mean1255.47453
Median Absolute Deviation (MAD)363.3
Skewness6.307921552
Sum597216679
Variance6604295.476
MonotonicityNot monotonic
2021-08-30T18:05:26.914879image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13767
 
< 0.1%
13563
 
< 0.1%
14063
 
< 0.1%
200.1561
 
< 0.1%
125.260
 
< 0.1%
3060
 
< 0.1%
157.160
 
< 0.1%
270.0559
 
< 0.1%
159.8558
 
< 0.1%
11258
 
< 0.1%
Other values (63729)475081
99.9%
ValueCountFrequency (%)
9.152
 
< 0.1%
9.42
 
< 0.1%
9.52
 
< 0.1%
9.62
 
< 0.1%
9.6512
< 0.1%
9.756
< 0.1%
9.84
 
< 0.1%
9.854
 
< 0.1%
9.954
 
< 0.1%
104
 
< 0.1%
ValueCountFrequency (%)
32861.952
< 0.1%
32766.12
< 0.1%
32661.852
< 0.1%
32617.752
< 0.1%
32498.62
< 0.1%
32443.52
< 0.1%
32403.952
< 0.1%
32371.052
< 0.1%
32233.42
< 0.1%
32226.42
< 0.1%

VWAP
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct138831
Distinct (%)29.2%
Missing50
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean1256.050425
Minimum9.21
Maximum32975.24
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.6 MiB
2021-08-30T18:05:27.140761image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum9.21
5-th percentile97.4645
Q1271.34
median558.4
Q31231.17
95-th percentile3946.8
Maximum32975.24
Range32966.03
Interquartile range (IQR)959.83

Descriptive statistics

Standard deviation2570.439218
Coefficient of variation (CV)2.046445881
Kurtosis49.35536364
Mean1256.050425
Median Absolute Deviation (MAD)363.305
Skewness6.307368685
Sum597490626.6
Variance6607157.771
MonotonicityNot monotonic
2021-08-30T18:05:27.353644image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
136.4925
 
< 0.1%
277.7624
 
< 0.1%
137.0724
 
< 0.1%
300.423
 
< 0.1%
259.7523
 
< 0.1%
135.3522
 
< 0.1%
141.8222
 
< 0.1%
186.9722
 
< 0.1%
163.0221
 
< 0.1%
168.0621
 
< 0.1%
Other values (138821)475463
99.9%
(Missing)50
 
< 0.1%
ValueCountFrequency (%)
9.212
 
< 0.1%
9.452
 
< 0.1%
9.612
 
< 0.1%
9.622
 
< 0.1%
9.652
 
< 0.1%
9.662
 
< 0.1%
9.686
< 0.1%
9.722
 
< 0.1%
9.752
 
< 0.1%
9.762
 
< 0.1%
ValueCountFrequency (%)
32975.242
< 0.1%
32882.472
< 0.1%
32599.82
< 0.1%
32581.182
< 0.1%
32556.122
< 0.1%
32540.72
< 0.1%
32458.752
< 0.1%
32437.362
< 0.1%
32393.752
< 0.1%
32304.712
< 0.1%

Volume
Real number (ℝ≥0)

HIGH CORRELATION

Distinct220434
Distinct (%)46.3%
Missing50
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean3065752.612
Minimum3
Maximum481058927
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.6 MiB
2021-08-30T18:05:27.577510image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile10586
Q1224287
median1026758
Q33038475
95-th percentile12158159
Maximum481058927
Range481058924
Interquartile range (IQR)2814188

Descriptive statistics

Standard deviation7349109.321
Coefficient of variation (CV)2.397163194
Kurtosis366.0976462
Mean3065752.612
Median Absolute Deviation (MAD)952379
Skewness12.35294776
Sum1.45834786 × 1012
Variance5.400940781 × 1013
MonotonicityNot monotonic
2021-08-30T18:05:27.803383image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10062
 
< 0.1%
20048
 
< 0.1%
100042
 
< 0.1%
50036
 
< 0.1%
30034
 
< 0.1%
70034
 
< 0.1%
90032
 
< 0.1%
140028
 
< 0.1%
80028
 
< 0.1%
40028
 
< 0.1%
Other values (220424)475318
99.9%
(Missing)50
 
< 0.1%
ValueCountFrequency (%)
32
 
< 0.1%
62
 
< 0.1%
102
 
< 0.1%
254
< 0.1%
274
< 0.1%
322
 
< 0.1%
332
 
< 0.1%
352
 
< 0.1%
508
< 0.1%
514
< 0.1%
ValueCountFrequency (%)
4810589272
< 0.1%
4797162452
< 0.1%
3905778392
< 0.1%
3160086092
< 0.1%
2868576582
< 0.1%
2861736292
< 0.1%
2836144632
< 0.1%
2709680282
< 0.1%
2657093912
< 0.1%
2626770812
< 0.1%

Turnover
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct236251
Distinct (%)49.7%
Missing50
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean1.606111268 × 1014
Minimum10470000
Maximum3.564334168 × 1016
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.6 MiB
2021-08-30T18:05:28.027242image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum10470000
5-th percentile3.7745247 × 1011
Q11.632697149 × 1013
median6.802326423 × 1013
Q31.856753031 × 1014
95-th percentile5.88649934 × 1014
Maximum3.564334168 × 1016
Range3.564334167 × 1016
Interquartile range (IQR)1.693483316 × 1014

Descriptive statistics

Standard deviation3.288006109 × 1014
Coefficient of variation (CV)2.04718451
Kurtosis912.6613985
Mean1.606111268 × 1014
Median Absolute Deviation (MAD)6.204383775 × 1013
Skewness16.14879937
Sum7.640110689 × 1019
Variance1.081098417 × 1029
MonotonicityNot monotonic
2021-08-30T18:05:28.245117image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.291994716 × 10135
 
< 0.1%
2600000004
 
< 0.1%
2975000004
 
< 0.1%
45278400004
 
< 0.1%
2750000004
 
< 0.1%
15350000004
 
< 0.1%
1.8760005 × 10114
 
< 0.1%
2800000004
 
< 0.1%
1.665872142 × 10133
 
< 0.1%
3.413466802 × 10143
 
< 0.1%
Other values (236241)475651
> 99.9%
(Missing)50
 
< 0.1%
ValueCountFrequency (%)
104700002
< 0.1%
257000002
< 0.1%
279000002
< 0.1%
630000002
< 0.1%
884500002
< 0.1%
959500002
< 0.1%
975000002
< 0.1%
1563000002
< 0.1%
1775000002
< 0.1%
1795000002
< 0.1%
ValueCountFrequency (%)
3.564334168 × 10162
< 0.1%
2.481773122 × 10162
< 0.1%
1.570039582 × 10162
< 0.1%
1.498221906 × 10162
< 0.1%
1.473433631 × 10162
< 0.1%
1.461958918 × 10162
< 0.1%
1.426399959 × 10162
< 0.1%
1.268361556 × 10162
< 0.1%
1.236079553 × 10162
< 0.1%
1.184668763 × 10162
< 0.1%

Trades
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct79162
Distinct (%)16.6%
Missing50
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean62980.19751
Minimum11
Maximum1643015
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.6 MiB
2021-08-30T18:05:28.481982image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile7411
Q137069.25
median61964.27098
Q365386
95-th percentile145230.6409
Maximum1643015
Range1643004
Interquartile range (IQR)28316.75

Descriptive statistics

Standard deviation52112.14655
Coefficient of variation (CV)0.8274370137
Kurtosis56.68163002
Mean62980.19751
Median Absolute Deviation (MAD)19592.27098
Skewness4.9989478
Sum2.995905015 × 1010
Variance2715675818
MonotonicityNot monotonic
2021-08-30T18:05:28.696859image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
61964.27098114848
 
24.1%
62645.709285700
 
1.2%
138367.62742850
 
0.6%
82923.625412850
 
0.6%
111849.03012850
 
0.6%
37121.18732850
 
0.6%
78354.908792850
 
0.6%
120602.23172850
 
0.6%
150702.76752850
 
0.6%
55511.070032850
 
0.6%
Other values (79152)332342
69.9%
ValueCountFrequency (%)
112
< 0.1%
162
< 0.1%
182
< 0.1%
202
< 0.1%
212
< 0.1%
242
< 0.1%
262
< 0.1%
272
< 0.1%
284
< 0.1%
302
< 0.1%
ValueCountFrequency (%)
16430152
< 0.1%
14284902
< 0.1%
14247932
< 0.1%
13186692
< 0.1%
13038252
< 0.1%
12855332
< 0.1%
12330532
< 0.1%
12059842
< 0.1%
12019282
< 0.1%
11940592
< 0.1%

Deliverable Volume
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct200001
Distinct (%)42.0%
Missing50
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean1319019.123
Minimum5
Maximum232530747
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.6 MiB
2021-08-30T18:05:28.928726image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile9831
Q1143455
median570927
Q31426126
95-th percentile4945056
Maximum232530747
Range232530742
Interquartile range (IQR)1282671

Descriptive statistics

Standard deviation2733060.883
Coefficient of variation (CV)2.072040379
Kurtosis987.5542677
Mean1319019.123
Median Absolute Deviation (MAD)514847
Skewness19.09900594
Sum6.274442065 × 1011
Variance7.46962179 × 1012
MonotonicityNot monotonic
2021-08-30T18:05:29.146601image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1315098.40816077
 
3.4%
1415717.6281038
 
0.2%
999465.3974528
 
0.1%
2551708.554525
 
0.1%
4183406.037517
 
0.1%
1331770.503515
 
0.1%
4571757.617514
 
0.1%
440876.3015514
 
0.1%
1550749.808514
 
0.1%
2280885.189514
 
0.1%
Other values (199991)454434
95.5%
ValueCountFrequency (%)
52
 
< 0.1%
62
 
< 0.1%
172
 
< 0.1%
256
< 0.1%
302
 
< 0.1%
322
 
< 0.1%
502
 
< 0.1%
542
 
< 0.1%
572
 
< 0.1%
582
 
< 0.1%
ValueCountFrequency (%)
2325307472
< 0.1%
2163778202
< 0.1%
1875849052
< 0.1%
1856563542
< 0.1%
1813901712
< 0.1%
1709462752
< 0.1%
1483131092
< 0.1%
1399438052
< 0.1%
1393453652
< 0.1%
1355720902
< 0.1%

%Deliverble
Real number (ℝ≥0)

HIGH CORRELATION

Distinct9638
Distinct (%)2.0%
Missing50
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean0.5022203801
Minimum0.0236
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.6 MiB
2021-08-30T18:05:29.366475image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0.0236
5-th percentile0.1863
Q10.3728
median0.5029968487
Q30.6294
95-th percentile0.8029
Maximum1
Range0.9764
Interquartile range (IQR)0.2566

Descriptive statistics

Standard deviation0.184320399
Coefficient of variation (CV)0.3670109902
Kurtosis-0.3812012965
Mean0.5022203801
Median Absolute Deviation (MAD)0.1280968487
Skewness-0.02155004166
Sum238901.2126
Variance0.03397400948
MonotonicityNot monotonic
2021-08-30T18:05:29.596344image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.502996848716077
 
3.4%
0.45221125971038
 
0.2%
0.5562428213528
 
0.1%
0.4340255386525
 
0.1%
0.4734629777517
 
0.1%
0.6031712795515
 
0.1%
0.4350394407514
 
0.1%
0.591634328514
 
0.1%
0.5092087855514
 
0.1%
0.3679444491514
 
0.1%
Other values (9628)454434
95.5%
ValueCountFrequency (%)
0.02362
< 0.1%
0.02792
< 0.1%
0.02832
< 0.1%
0.02942
< 0.1%
0.02994
< 0.1%
0.03182
< 0.1%
0.03262
< 0.1%
0.03282
< 0.1%
0.03434
< 0.1%
0.03582
< 0.1%
ValueCountFrequency (%)
1189
< 0.1%
0.99982
 
< 0.1%
0.99974
 
< 0.1%
0.999610
 
< 0.1%
0.99956
 
< 0.1%
0.99932
 
< 0.1%
0.99926
 
< 0.1%
0.9991
 
< 0.1%
0.9991
 
< 0.1%
0.99892
 
< 0.1%

Company Name
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING
UNIFORM

Distinct50
Distinct (%)100.0%
Missing475690
Missing (%)> 99.9%
Memory size14.5 MiB
Larsen & Toubro Ltd.
 
1
NTPC Ltd.
 
1
Indian Oil Corporation Ltd.
 
1
Bharti Airtel Ltd.
 
1
UltraTech Cement Ltd.
 
1
Other values (45)
45 

Length

Max length44
Median length18
Mean length20.66
Min length8

Characters and Unicode

Total characters1033
Distinct characters54
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique50 ?
Unique (%)100.0%

Sample

1st rowAdani Ports and Special Economic Zone Ltd.
2nd rowAsian Paints Ltd.
3rd rowAxis Bank Ltd.
4th rowBajaj Auto Ltd.
5th rowBajaj Finserv Ltd.

Common Values

ValueCountFrequency (%)
Larsen & Toubro Ltd.1
 
< 0.1%
NTPC Ltd.1
 
< 0.1%
Indian Oil Corporation Ltd.1
 
< 0.1%
Bharti Airtel Ltd.1
 
< 0.1%
UltraTech Cement Ltd.1
 
< 0.1%
HDFC Bank Ltd.1
 
< 0.1%
Nestle India Ltd.1
 
< 0.1%
Kotak Mahindra Bank Ltd.1
 
< 0.1%
IndusInd Bank Ltd.1
 
< 0.1%
Coal India Ltd.1
 
< 0.1%
Other values (40)40
 
< 0.1%
(Missing)475690
> 99.9%

Length

2021-08-30T18:05:30.075069image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ltd49
29.5%
india6
 
3.6%
bank6
 
3.6%
corporation5
 
3.0%
industries5
 
3.0%
mahindra4
 
2.4%
3
 
1.8%
bajaj3
 
1.8%
tata3
 
1.8%
bharti2
 
1.2%
Other values (74)80
48.2%

Most occurring characters

ValueCountFrequency (%)
116
 
11.2%
t97
 
9.4%
a77
 
7.5%
d75
 
7.3%
n66
 
6.4%
i56
 
5.4%
e54
 
5.2%
L54
 
5.2%
r52
 
5.0%
.50
 
4.8%
Other values (44)336
32.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter677
65.5%
Uppercase Letter184
 
17.8%
Space Separator116
 
11.2%
Other Punctuation54
 
5.2%
Open Punctuation1
 
0.1%
Close Punctuation1
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t97
14.3%
a77
11.4%
d75
11.1%
n66
9.7%
i56
8.3%
e54
8.0%
r52
7.7%
o47
6.9%
s33
 
4.9%
l20
 
3.0%
Other values (15)100
14.8%
Uppercase Letter
ValueCountFrequency (%)
L54
29.3%
I21
 
11.4%
C18
 
9.8%
B13
 
7.1%
T10
 
5.4%
S9
 
4.9%
M8
 
4.3%
P7
 
3.8%
A6
 
3.3%
H6
 
3.3%
Other values (13)32
17.4%
Other Punctuation
ValueCountFrequency (%)
.50
92.6%
&3
 
5.6%
'1
 
1.9%
Space Separator
ValueCountFrequency (%)
116
100.0%
Open Punctuation
ValueCountFrequency (%)
(1
100.0%
Close Punctuation
ValueCountFrequency (%)
)1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin861
83.3%
Common172
 
16.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
t97
 
11.3%
a77
 
8.9%
d75
 
8.7%
n66
 
7.7%
i56
 
6.5%
e54
 
6.3%
L54
 
6.3%
r52
 
6.0%
o47
 
5.5%
s33
 
3.8%
Other values (38)250
29.0%
Common
ValueCountFrequency (%)
116
67.4%
.50
29.1%
&3
 
1.7%
'1
 
0.6%
(1
 
0.6%
)1
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII1033
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
116
 
11.2%
t97
 
9.4%
a77
 
7.5%
d75
 
7.3%
n66
 
6.4%
i56
 
5.4%
e54
 
5.2%
L54
 
5.2%
r52
 
5.0%
.50
 
4.8%
Other values (44)336
32.5%

Industry
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct13
Distinct (%)26.0%
Missing475690
Missing (%)> 99.9%
Memory size14.5 MiB
FINANCIAL SERVICES
ENERGY
CONSUMER GOODS
AUTOMOBILE
METALS
Other values (8)
17 

Length

Max length24
Median length10
Mean length11.14
Min length2

Characters and Unicode

Total characters557
Distinct characters22
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)8.0%

Sample

1st rowSERVICES
2nd rowCONSUMER GOODS
3rd rowFINANCIAL SERVICES
4th rowAUTOMOBILE
5th rowFINANCIAL SERVICES

Common Values

ValueCountFrequency (%)
FINANCIAL SERVICES9
 
< 0.1%
ENERGY7
 
< 0.1%
CONSUMER GOODS6
 
< 0.1%
AUTOMOBILE6
 
< 0.1%
METALS5
 
< 0.1%
IT5
 
< 0.1%
CEMENT & CEMENT PRODUCTS3
 
< 0.1%
PHARMA3
 
< 0.1%
TELECOM2
 
< 0.1%
SERVICES1
 
< 0.1%
Other values (3)3
 
< 0.1%
(Missing)475690
> 99.9%

Length

2021-08-30T18:05:30.523812image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
services10
12.8%
financial9
11.5%
energy7
9.0%
goods6
7.7%
consumer6
7.7%
automobile6
7.7%
cement6
7.7%
metals5
 
6.4%
it5
 
6.4%
5
 
6.4%
Other values (8)13
16.7%

Most occurring characters

ValueCountFrequency (%)
E75
13.5%
I46
 
8.3%
S45
 
8.1%
N42
 
7.5%
C39
 
7.0%
O37
 
6.6%
A37
 
6.6%
T34
 
6.1%
R33
 
5.9%
M30
 
5.4%
Other values (12)139
25.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter524
94.1%
Space Separator28
 
5.0%
Other Punctuation5
 
0.9%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E75
14.3%
I46
8.8%
S45
8.6%
N42
 
8.0%
C39
 
7.4%
O37
 
7.1%
A37
 
7.1%
T34
 
6.5%
R33
 
6.3%
M30
 
5.7%
Other values (10)106
20.2%
Space Separator
ValueCountFrequency (%)
28
100.0%
Other Punctuation
ValueCountFrequency (%)
&5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin524
94.1%
Common33
 
5.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
E75
14.3%
I46
8.8%
S45
8.6%
N42
 
8.0%
C39
 
7.4%
O37
 
7.1%
A37
 
7.1%
T34
 
6.5%
R33
 
6.3%
M30
 
5.7%
Other values (10)106
20.2%
Common
ValueCountFrequency (%)
28
84.8%
&5
 
15.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII557
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E75
13.5%
I46
 
8.3%
S45
 
8.1%
N42
 
7.5%
C39
 
7.0%
O37
 
6.6%
A37
 
6.6%
T34
 
6.1%
R33
 
5.9%
M30
 
5.4%
Other values (12)139
25.0%

ISIN Code
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING
UNIFORM

Distinct50
Distinct (%)100.0%
Missing475690
Missing (%)> 99.9%
Memory size14.5 MiB
INE001A01036
 
1
INE075A01022
 
1
INE019A01038
 
1
INE213A01029
 
1
INE481G01011
 
1
Other values (45)
45 

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters600
Distinct characters20
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique50 ?
Unique (%)100.0%

Sample

1st rowINE742F01042
2nd rowINE021A01026
3rd rowINE238A01034
4th rowINE917I01010
5th rowINE918I01018

Common Values

ValueCountFrequency (%)
INE001A010361
 
< 0.1%
INE075A010221
 
< 0.1%
INE019A010381
 
< 0.1%
INE213A010291
 
< 0.1%
INE481G010111
 
< 0.1%
INE628A010361
 
< 0.1%
INE216A010301
 
< 0.1%
INE918I010181
 
< 0.1%
INE129A010191
 
< 0.1%
INE205A010251
 
< 0.1%
Other values (40)40
 
< 0.1%
(Missing)475690
> 99.9%

Length

2021-08-30T18:05:30.940573image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ine752e010101
 
2.0%
ine239a010161
 
2.0%
ine062a010201
 
2.0%
ine237a010281
 
2.0%
ine522f010141
 
2.0%
ine021a010261
 
2.0%
ine213a010291
 
2.0%
ine075a010221
 
2.0%
ine917i010101
 
2.0%
ine154a010251
 
2.0%
Other values (40)40
80.0%

Most occurring characters

ValueCountFrequency (%)
0141
23.5%
190
15.0%
I52
 
8.7%
E52
 
8.7%
N50
 
8.3%
250
 
8.3%
A38
 
6.3%
620
 
3.3%
320
 
3.3%
818
 
3.0%
Other values (10)69
11.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number400
66.7%
Uppercase Letter200
33.3%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
I52
26.0%
E52
26.0%
N50
25.0%
A38
19.0%
F2
 
1.0%
B2
 
1.0%
D1
 
0.5%
J1
 
0.5%
C1
 
0.5%
G1
 
0.5%
Decimal Number
ValueCountFrequency (%)
0141
35.2%
190
22.5%
250
 
12.5%
620
 
5.0%
320
 
5.0%
818
 
4.5%
917
 
4.2%
516
 
4.0%
415
 
3.8%
713
 
3.2%

Most occurring scripts

ValueCountFrequency (%)
Common400
66.7%
Latin200
33.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
I52
26.0%
E52
26.0%
N50
25.0%
A38
19.0%
F2
 
1.0%
B2
 
1.0%
D1
 
0.5%
J1
 
0.5%
C1
 
0.5%
G1
 
0.5%
Common
ValueCountFrequency (%)
0141
35.2%
190
22.5%
250
 
12.5%
620
 
5.0%
320
 
5.0%
818
 
4.5%
917
 
4.2%
516
 
4.0%
415
 
3.8%
713
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII600
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0141
23.5%
190
15.0%
I52
 
8.7%
E52
 
8.7%
N50
 
8.3%
250
 
8.3%
A38
 
6.3%
620
 
3.3%
320
 
3.3%
818
 
3.0%
Other values (10)69
11.5%

Interactions

2021-08-30T18:03:56.978780image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-30T18:03:57.409534image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-30T18:03:57.849281image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-30T18:03:58.282033image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-30T18:03:58.708790image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-30T18:03:59.127548image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-30T18:03:59.553305image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-30T18:03:59.977062image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-30T18:04:00.451429image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-30T18:04:00.879184image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-30T18:04:01.321929image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-30T18:04:01.747685image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-30T18:04:02.182436image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-30T18:04:02.603215image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-30T18:04:03.021955image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-30T18:04:03.463703image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-30T18:04:03.898469image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-30T18:04:04.334216image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-30T18:04:04.761958image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-30T18:04:05.188715image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-30T18:04:05.619482image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-30T18:04:06.103202image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-30T18:04:06.540940image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-30T18:04:06.992682image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-30T18:04:07.427447image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-30T18:04:07.878192image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-30T18:04:08.302942image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-30T18:04:08.727688image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-30T18:04:09.162438image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-30T18:04:09.610182image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-30T18:04:10.053931image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-30T18:04:10.492676image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-30T18:04:10.927444image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-30T18:04:11.362179image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-30T18:04:11.854896image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-30T18:04:12.287648image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-30T18:04:12.741409image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-30T18:04:13.186134image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-30T18:04:13.637886image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-30T18:04:14.076640image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-30T18:04:14.502379image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-30T18:04:14.936130image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-30T18:04:15.382886image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-30T18:04:15.830618image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-30T18:04:16.266369image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-30T18:04:16.706128image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-30T18:04:17.141883image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-30T18:04:17.630587image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-30T18:04:18.072334image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-30T18:04:18.525075image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-30T18:04:18.962836image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-30T18:04:19.417564image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-30T18:04:19.865319image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-30T18:04:20.282069image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-30T18:04:20.711833image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-30T18:04:21.153570image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-30T18:04:21.584335image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-30T18:04:22.017075image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-30T18:04:22.446843image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-30T18:04:22.882579image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-30T18:04:23.365314image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-30T18:04:23.793058image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-30T18:04:24.234804image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-30T18:04:24.668567image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-30T18:04:25.120415image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-30T18:04:25.553167image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-30T18:04:25.984935image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-30T18:04:26.422670image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-30T18:04:26.867415image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-30T18:04:27.309161image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-30T18:04:27.740935image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-30T18:04:28.167670image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-30T18:04:28.609417image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-30T18:04:29.091140image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-30T18:04:29.519895image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-30T18:04:29.964652image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-30T18:04:30.711213image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-30T18:04:31.157957image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-30T18:04:31.592708image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-30T18:04:32.014467image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-30T18:04:32.444220image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-30T18:04:32.887965image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-30T18:04:33.329727image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-30T18:04:33.755469image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-30T18:04:34.185222image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-30T18:04:34.621973image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-30T18:04:35.099699image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-30T18:04:35.535450image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-30T18:04:35.986191image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-30T18:04:36.422956image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-30T18:04:36.878680image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-30T18:04:37.311432image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-30T18:04:37.728193image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-30T18:04:38.161945image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-30T18:04:38.598694image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-30T18:04:39.035444image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-30T18:04:39.461200image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-30T18:04:39.885972image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-30T18:04:40.319708image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-30T18:04:40.814425image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-30T18:04:41.253175image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-30T18:04:41.764881image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-30T18:04:42.211624image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-30T18:04:42.685354image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-30T18:04:43.187067image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-30T18:04:43.666792image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-30T18:04:44.151513image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-30T18:04:44.640234image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-30T18:04:45.133951image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-30T18:04:45.625669image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-30T18:04:46.112391image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-30T18:04:46.602130image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-30T18:04:47.095339image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-30T18:04:47.582060image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-30T18:04:48.082773image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-30T18:04:48.590483image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-30T18:04:49.086198image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-30T18:04:49.517966image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-30T18:04:49.953701image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-30T18:04:50.404443image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-30T18:04:50.856201image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-30T18:04:51.324916image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-30T18:04:51.786651image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-30T18:04:52.235394image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-30T18:04:52.684137image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-30T18:04:53.193856image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-30T18:04:53.646585image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-30T18:04:54.120315image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-30T18:04:54.580051image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-30T18:04:55.049793image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-30T18:04:55.503531image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-30T18:04:55.944269image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-30T18:04:56.389015image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-30T18:04:56.837758image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-30T18:04:57.292497image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-30T18:04:57.743238image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-30T18:04:58.550777image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-30T18:04:58.996522image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-30T18:04:59.496235image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-30T18:04:59.939981image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-30T18:05:00.393735image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-30T18:05:00.847461image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-30T18:05:01.305199image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-30T18:05:01.747946image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-30T18:05:02.163707image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-30T18:05:02.589463image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-30T18:05:03.018218image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-30T18:05:03.449985image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-30T18:05:03.883733image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-30T18:05:04.312475image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-30T18:05:04.743240image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-30T18:05:05.217957image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-30T18:05:05.640716image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-30T18:05:06.087460image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-30T18:05:06.521219image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-30T18:05:06.969954image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-30T18:05:07.416697image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-30T18:05:07.847471image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-30T18:05:08.294203image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-30T18:05:08.747951image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-30T18:05:09.195679image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-30T18:05:09.634439image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-30T18:05:10.076175image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-30T18:05:10.517921image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-30T18:05:11.008640image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-30T18:05:11.451387image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-30T18:05:11.904143image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-30T18:05:12.351871image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-30T18:05:12.794632image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2021-08-30T18:05:31.112475image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-08-30T18:05:31.455279image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-08-30T18:05:31.793097image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-08-30T18:05:32.150879image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-08-30T18:05:32.537674image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-08-30T18:05:13.788063image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
A simple visualization of nullity by column.
2021-08-30T18:05:15.213232image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2021-08-30T18:05:18.645600image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2021-08-30T18:05:20.139831image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

df_indexDateSymbolSeriesPrev CloseOpenHighLowLastCloseVWAPVolumeTurnoverTradesDeliverable Volume%DeliverbleCompany NameIndustryISIN Code
002000-01-03ZEETELEEQ1092.551175.001179.951160.001179.951179.951177.0312613911.484690e+1462645.7092831.415718e+060.452211NaNNaNNaN
112000-01-04ZEETELEEQ1179.951220.001274.351183.101274.351260.651228.0246165475.669220e+1462645.7092831.415718e+060.452211NaNNaNNaN
222000-01-05ZEETELEEQ1260.651160.551317.701159.801190.951176.551238.3587631271.085178e+1562645.7092831.415718e+060.452211NaNNaNNaN
332000-01-06ZEETELEEQ1176.551195.001200.001095.001106.001115.451135.0451640205.861353e+1462645.7092831.415718e+060.452211NaNNaNNaN
442000-01-07ZEETELEEQ1115.451097.101097.101026.251026.251026.251029.947551297.777374e+1362645.7092831.415718e+060.452211NaNNaNNaN
552000-01-10ZEETELEEQ1026.251026.251026.25944.30962.00966.70980.4939428133.865885e+1462645.7092831.415718e+060.452211NaNNaNNaN
662000-01-11ZEETELEEQ966.70985.001044.00891.001018.801019.95995.9768020056.774605e+1462645.7092831.415718e+060.452211NaNNaNNaN
772000-01-12ZEETELEEQ1019.951035.001088.001035.001064.001063.851068.9929688333.173653e+1462645.7092831.415718e+060.452211NaNNaNNaN
882000-01-13ZEETELEEQ1063.851073.001085.001012.701018.001023.101050.5722510462.364882e+1462645.7092831.415718e+060.452211NaNNaNNaN
992000-01-14ZEETELEEQ1023.101030.001034.75980.701001.001001.851007.7429490922.971906e+1462645.7092831.415718e+060.452211NaNNaNNaN

Last rows

df_indexDateSymbolSeriesPrev CloseOpenHighLowLastCloseVWAPVolumeTurnoverTradesDeliverable Volume%DeliverbleCompany NameIndustryISIN Code
47573052962021-04-16ZEELEQ189.20189.2196.90189.20196.35193.95193.1479522021.535854e+1452127.01962297.00.2468NaNNaNNaN
47573152972021-04-19ZEELEQ193.95187.0191.20185.10191.00190.35188.2893469561.759879e+1458935.01270493.00.1359NaNNaNNaN
47573252982021-04-20ZEELEQ190.35193.0201.75192.20197.30197.40197.17157091323.097437e+1477021.03089894.00.1967NaNNaNNaN
47573352992021-04-22ZEELEQ197.40195.5199.80191.70192.15192.30194.76102031211.987114e+1470603.02930009.00.2872NaNNaNNaN
47573453002021-04-23ZEELEQ192.30192.0193.00187.00188.00188.00190.0485294391.620902e+1460790.02148300.00.2519NaNNaNNaN
47573553012021-04-26ZEELEQ188.00190.6191.10185.10186.70186.40187.3585427551.600451e+1452374.02340188.00.2739NaNNaNNaN
47573653022021-04-27ZEELEQ186.40188.0192.95186.80188.80188.15189.41142477672.698636e+1473673.05425957.00.3808NaNNaNNaN
47573753032021-04-28ZEELEQ188.15188.8190.60187.10188.95189.10188.8584294391.591917e+1444056.02413974.00.2864NaNNaNNaN
47573853042021-04-29ZEELEQ189.10190.8191.65186.00186.60186.55187.4494830091.777471e+1460932.02744472.00.2894NaNNaNNaN
47573953052021-04-30ZEELEQ186.55185.3190.95183.65185.00185.60187.53114352852.144440e+1462607.03323909.00.2907NaNNaNNaN